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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
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Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
Law of Independent Assortment02:03

Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.

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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
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Published on: November 12, 2012

Assortative mixing in directed biological networks.

Mahendra Piraveenan1, Mikhail Prokopenko, Albert Zomaya

  • 1CSIRO ICT Centre, North Ryde.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|August 25, 2010
PubMed
Summary

Researchers developed new metrics to analyze assortative mixing in directed biological networks. These new measures reveal that many networks previously thought disassortative are actually assortative, offering new insights into network function.

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Area of Science:

  • Network science
  • Systems biology
  • Computational biology

Background:

  • Biological networks are typically directed, but existing analyses of mixing patterns often overlook directionality.
  • Understanding assortative mixing is crucial for comprehending network organization and function.

Purpose of the Study:

  • To develop a theoretical framework for analyzing mixing patterns in directed networks.
  • To introduce novel quantitative measures for assortative mixing in directed biological networks.
  • To re-evaluate the assortativity of biological networks using these new measures.

Main Methods:

  • Development of in-assortativity and out-assortativity metrics.
  • Introduction of local assortativity measures at the node level.
  • Analysis of canonical and real-world directed biological networks using local assortativity profiles.

Main Results:

  • The new metrics, in-assortativity and out-assortativity, effectively quantify mixing patterns in directed networks.
  • Local assortativity profiles provide detailed insights into node-level mixing behavior.
  • Many biological networks previously classified as disassortative exhibit assortative properties under the new framework.

Conclusions:

  • The developed metrics offer a more accurate characterization of assortative mixing in directed biological networks.
  • Reclassification of previously disassortative networks as assortative provides new perspectives on their structure and function.
  • Local assortativity profiles are valuable tools for investigating the functional roles of nodes and network modules.